The present invention relates generally to image processing, and more particularly, but not by way of limitation, to a system, a method, and a recording medium including inputting entities, outlier objects can be detected via efficient cohesive subgraph identification, and outputting two lists; outlier entities and inlier entities.
In conventional media collection containing facial imagery, often times there is only a small portion of the collection that is relevant to a person of interest. The rest of the collection is of zero value but adds a significant burden on the user or an analyst to be able to remove them from the collection.
Conventionally, outlier detection has been performed via geometric-based methods, such as PCA, Kernel PCA, Robust PCA, or Robust Kernel PCA.
Other conventional methods have performed outlier detection by probabilistic modeling method, such as, kernel density estimator (KDE) and robust kernel density estimator (RKDE).
Another conventional outlier detection method has proposed performing the outlier detection by a learning method such as one-class support vector machines (OC-SVMs) or support vector data description (SVDD).
However, each conventional outlier detection method above, and all other conventional outlier detection methods are limited in their application in that they have a high computational cost on a large dataset, require prior knowledge, and difficult to be extended to an online case.